Modeling user multiple interests by an improved GCS approach

1. Modeling user multiple interests by an improved GCS approach Advisor : Dr. Hsu Graduate : Kuo-min Wang Authors :Wu Lihua*, Liu Lu, Li Jing, Li Zongyong 2005 Expert Systems with Applications .

2. Outline • Motivation • Objective • Introduction • Background and related work • IGCS approach • Experiments • Conclusion • Personal Opinions

3. Motivate • User interest profile is the crucial component of most personalized recommender systems. • The diversity and time-dependent evolving nature of user interests are creating difficulties in constructing and maintaining a sound user profile.

4. Objective • This paper presents a simple but effective model to address this problem.

5. Introduction • Information overload problem • Users have to spend much time and effort on information search and selection before they can find out what they want. • Personalized recommender system • Summarizes and generalizes a user’s interests, to filter away irrelevant information • Only information most catering for user’s interests is advised to him/her.

6. Introduction (cont.) • Statistical keyword analysis • Represents user profile as a vector of keyword and its weight pairs. The keyword in user profile can be calculated from the TF-IDF • Drawback • Combines all interests of a user into a single vector. It is insufficient and ineffective to generalize all kinds of interest by a single vector. • This method assumes that user interests change only infrequently, so the user profile is adapted at a fixed, predetermined pace. • Can not change user’s interest continuously and rapidly.

7. Introduction (cont.) • We present a neural network-based approach for automatically learning and maintaining user multiple interests. • The proposed model utilizes improved growing cell structures (IGCS) to group user-interested information in to separate topic categories.

8. Background and related work • Modeling of user multiple interests • Neural networks for information recommendation

9. Modeling of user multiple interests • Amalthaea (Moukas, 1997) & Fab (Balabanovic & Shoham, 1997) • Topic information collection and answered for user interest maintenance. • Both systems updated user profile by an evolution process of agents based on user’s relevant feedback. • A high user involvement is required for updating the profile in Amathaea.

10. Modeling of user multiple interests (cont.) • Cetintemel, Franklin, and Giles (2000) • Widyantoro, Ioerger, and Yen (2001) • Combined both the long-term interest and short-term interest of a user into the interest category representation by a weighted average scheme. • Drawback • They also cost a great deal of user’s effort since they need user’s explicitly relevant feedback to maintain user profile.

11. Neural networks for information recommendation (cont.) • Adaptive resonance associative map(ARAM)(Tan & Teo, 1998) • A user profile was modeled by first classing information into categories by the ARAM network. • Then each of the information categories characterized by semantic features was associated with a relevance factor indicated by the user to acquire the user profile.

12. Neural networks for information recommendation (cont.) • Self-organizing map • Drawback • Cannot adapt to new input pattern after the learning period • User has to define a number of parameters including learning rates and neighborhood size which decay over time in the learning process • GCS (Fritzke, 1994)

13. IGCS approach • Learning user unchanged interests • adding new interests to current profile • removing outdated interests from current profile

14. The properties of the IGCS • Category representation of user profile • user profile is represented by a collection of interest clusters recognized by the neural network. • Learning user interests in an incremental and real-time fashion • IGCS approach is incremental since it receives new case individually and modifies the user profile accordingly • Robust learning and generalization inference ability • Properly learn user interests even with partial or inconsistent information • Visualization clustering analysis

15. Learning user unchanged interests (1) increment input counter, N, which numbers the input vector: (2) select the best matching unit busing Euclidean distance between input x and the weight vectors of all output cells.

16. Learning user unchanged interests (cont.) (3) adapt weight vector w for b and its direct topological neighbors Nb[0,05 0.1] forεb[0.002 0.01] forεn (4) update the winning counter for the winner cell b (5) compute the difference between the input counter , N, and the winning counter, λ

17. Adding new interests • The new cell is connected to the first two winners for x and, if there is any, to their common direct neighbor too.

18. Removing outdated interests • Deletion of a cell • If the difference, rjmaintained for cell j exceeds a removal threshold, rd, namely then the cell j and all its adjacent edges are removed.

19. Experiments • Data collection • RCV1-v2(Lewis, Yang, Rose, & Li, 2004) is a new benchmark collection for text categorization. • Three category sets • Topic(103), Industries(354), and Regions(366) • contains 804,414 documents • We randomly selected 20 categories from the 103 topic categories. • Training set comprising 1500 documents • Testing set comprising the rest 500 documents

20. Experiments • Performance metrics • F1 measure • Intra-cluster distance • Inter-cluster distance

21. Experiments

22. Conclusion • we designed a clustering model based on the growing cell structures neural network to learn user’s multiple interests and their changes over time. • The proposed model can adaptively find a suitable category number for user interests • And capture the time-dependent evolving nature of the user interests.

23. Conclusion • Further research • Investigation on the online adaptability of IGCS model • Further experiments should be made on some representative samples to see whether the selection of sample data does influence the performance of our model.

24. Personal Opinions • …